Exploiting Adapters for Cross-lingual Low-resource Speech Recognition (2105.11905v2)

Abstract: Cross-lingual speech adaptation aims to solve the problem of leveraging multiple rich-resource languages to build models for a low-resource target language. Since the low-resource language has limited training data, speech recognition models can easily overfit. In this paper, we propose to use adapters to investigate the performance of multiple adapters for parameter-efficient cross-lingual speech adaptation. Based on our previous MetaAdapter that implicitly leverages adapters, we propose a novel algorithms called SimAdapter for explicitly learning knowledge from adapters. Our algorithm leverages adapters which can be easily integrated into the Transformer structure.MetaAdapter leverages meta-learning to transfer the general knowledge from training data to the test language. SimAdapter aims to learn the similarities between the source and target languages during fine-tuning using the adapters. We conduct extensive experiments on five-low-resource languages in Common Voice dataset. Results demonstrate that our MetaAdapter and SimAdapter methods can reduce WER by 2.98% and 2.55% with only 2.5% and 15.5% of trainable parameters compared to the strong full-model fine-tuning baseline. Moreover, we also show that these two novel algorithms can be integrated for better performance with up to 3.55% relative WER reduction.

Review of 'Exploiting Adapters for Cross-lingual Low-resource Speech Recognition'

The paper "Exploiting Adapters for Cross-lingual Low-resource Speech Recognition" presents a detailed exploration of utilizing adapter modules to enhance the adaptability and efficiency of speech recognition systems across multiple languages, particularly focusing on low-resource scenarios. The research introduces two novel approaches, MetaAdapter and SimAdapter, designed to leverage existing rich-resource LLMs to improve the performance of Transformers on low-resource languages.

Technical Contributions

MetaAdapter:

The MetaAdapter approach utilizes model-agnostic meta-learning (MAML) to learn general, transferable representations across different rich-resource languages. The critical advantage here is the initialization process for adapting a pre-trained model to a low-resource language. By focusing on the adapters alone, MetaAdapter manages to drastically reduce the number of trainable parameters, addressing the issue of overfitting commonly faced when working with limited data.

SimAdapter:

SimAdapter, on the other hand, introduces an attention-based mechanism to explicitly learn and exploit inter-language similarities during the adaptation process. This method seeks to identify and utilize shared linguistic structures and phonetic similarities among languages, leveraging attention scores to dynamically integrate knowledge from multiple adapters.

Both techniques are demonstrated to be successful, reducing WER by 2.98% and 2.55% utilizing only 2.5% and 15.5% of trainable parameters respectively, compared to conventional full-model fine-tuning.

Experimental Results

Experiments were conducted using five low-resource languages from the Common Voice dataset. The substantial reduction in WER and a decrease in trainable parameters highlight the practical efficiency of the MetaAdapter and SimAdapter approaches. The paper further finds that integrating these two methods (SimAdapter+) yields even better performance, up to a 3.55% relative WER reduction.

Implications and Future Directions

The paper's implications are significant for AI in the area of multilingual speech recognition. MetaAdapter and SimAdapter present viable solutions for enhancing speech models where data scarcity is a prominent issue. The usage of adapters is a promising avenue in balancing performance and computational resource demands.

The results encourage further exploration into extending these adapter-based approaches beyond the European language family to more diverse linguistic settings. Future work could investigate the scalability of these methods and explore additional techniques to refine cross-lingual adapter training.

Overall, this research contributes robust methodologies for cross-lingual speech adaptation, bridging a crucial gap in multilingual low-resource settings and paving the way for more inclusive and efficient speech recognition systems globally.